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3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding

Yihao Ding, Lorenzo Vaiani, Caren Han, Jean Lee, Paolo Garza, Josiah Poon, Luca Cagliero

TL;DR

3MVRD tackles visually-rich form document understanding by unifying fine-grained token and coarse-grained entity representations through a joint-grained, multimodal framework. It employs multimodal multi-task multi-teacher knowledge distillation with intra-grained (similarity, distillation) and cross-grained (triplet, alignment) losses to fuse knowledge from diverse teachers. Evaluations on FUNSD and FormNLU show the approach yields strong improvements over single-teacher baselines and demonstrates robust token-entity correlation and cross-grained transfer. This framework advances practical form understanding by leveraging multiple specialized teachers and targeted losses to capture both detailed and high-level document structure.

Abstract

This paper presents a groundbreaking multimodal, multi-task, multi-teacher joint-grained knowledge distillation model for visually-rich form document understanding. The model is designed to leverage insights from both fine-grained and coarse-grained levels by facilitating a nuanced correlation between token and entity representations, addressing the complexities inherent in form documents. Additionally, we introduce new inter-grained and cross-grained loss functions to further refine diverse multi-teacher knowledge distillation transfer process, presenting distribution gaps and a harmonised understanding of form documents. Through a comprehensive evaluation across publicly available form document understanding datasets, our proposed model consistently outperforms existing baselines, showcasing its efficacy in handling the intricate structures and content of visually complex form documents.

3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding

TL;DR

3MVRD tackles visually-rich form document understanding by unifying fine-grained token and coarse-grained entity representations through a joint-grained, multimodal framework. It employs multimodal multi-task multi-teacher knowledge distillation with intra-grained (similarity, distillation) and cross-grained (triplet, alignment) losses to fuse knowledge from diverse teachers. Evaluations on FUNSD and FormNLU show the approach yields strong improvements over single-teacher baselines and demonstrates robust token-entity correlation and cross-grained transfer. This framework advances practical form understanding by leveraging multiple specialized teachers and targeted losses to capture both detailed and high-level document structure.

Abstract

This paper presents a groundbreaking multimodal, multi-task, multi-teacher joint-grained knowledge distillation model for visually-rich form document understanding. The model is designed to leverage insights from both fine-grained and coarse-grained levels by facilitating a nuanced correlation between token and entity representations, addressing the complexities inherent in form documents. Additionally, we introduce new inter-grained and cross-grained loss functions to further refine diverse multi-teacher knowledge distillation transfer process, presenting distribution gaps and a harmonised understanding of form documents. Through a comprehensive evaluation across publicly available form document understanding datasets, our proposed model consistently outperforms existing baselines, showcasing its efficacy in handling the intricate structures and content of visually complex form documents.
Paper Structure (25 sections, 9 equations, 3 figures, 9 tables)

This paper contains 25 sections, 9 equations, 3 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodology
  4. Preliminary Definitions
  5. Multimodal Multi-task Multi-teacher Joint-grained Document Understanding
  6. Multimodal Multi-task Multi-Teacher
  7. Joint-Grained Learning
  8. Multimodal Multi-task Multi-Teacher Knowledge Distillation
  9. Task-oriented Cross Entropy Loss
  10. Intra-Grained Loss Functions
  11. Cross-Grained Loss Functions
  12. Evaluation Setup
  13. Datasets
  14. Baselines and Metrics
  15. Implementation Details
  16. ...and 10 more sections

Figures (3)

  • Figure 1: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding (3MVRD). Each section is aligned with the specific colours, Green: Section \ref{['sec:mmmt']}, Blue: Section \ref{['sec:jointl']}, Orange: Section \ref{['sec:mmm_loss']}
  • Figure 2: Example output showing (a) Ground Truth (b) JG-$\mathcal{E}$&$\mathcal{D}$ (c) LayoutLMv3, and (d) Ours on a FUNSD page. The color code for layout component labels is as follows; Question, Answer, Header, Other. Our model, employing the best loss combination (cross-entropy + similarity) on FUNSD, accurately classified all layout components.
  • Figure 3: Example output showing (a) Ground Truth (b) LayoutLMv3, and (c) Ours on a FormNLU handwritten test set. The color code for layout component labels is as follows; Title, Section, Form Key, Form Value, Table Key, Table Value, Other. Our model, the best loss combination (+Sim+Distil+Triplet+Align) on FormNLU H, accurately classified all layout components.